AI-Based Fine Morphological Subtyping of Myeloma Single Cells for Predicting FISH Abnormalities

Enrolling by invitation

Conditions studied: Multiple Myeloma

In brief

This study developed an artificial intelligence (AI)-based methodology for the quantitative analysis of single-cell morphological data in multiple myeloma (MM). The approach achieves high-precision AI-driven identification and segmentation of myeloma cells, nuclei, cytoplasm, and nucleoli, overcoming the inherent limitations of subjective traditional morphological analysis. Furthermore, integrating this morphological quantification with cytogenetic abnormality analysis of myeloma cells provides an efficient predictive tool for identifying high-risk cytogenetic abnormalities. Leveraging AI-guided selection of genetic testing targets, the research applied a rapid genetic abnormality detection technique utilizing first-drop bone marrow aspirate smears. This methodology achieves orders of magnitude improvements in testing cost, sample preprocessing time and detection sensitivity.

Key facts

Study ID

NCT07410403

Run by

Fuling Zhou

People needed

10

Starts

2018-08-01

Expected to finish

2026-12-31

Last updated by the study team

2026-02-13

Who can join

Age: any. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

• according to the criteria outlined in the Chinese guidelines for MM diagnosis and management (2024 edition)

You may not qualify if…

• non-MM

Where it is running

• Zhongnan Hospital of Wuhan University — Wuhan, Hubei, China

Full record on ClinicalTrials.gov

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